Inference of the optical depth to reionization τ from Planck CMB maps with convolutional neural networks

¹ International School for Advanced Studies (SISSA), Via Bonomea 265, 34136 Trieste, Italy
e-mail: kwolz@sissa.it; nkrach@sissa.it
² National Institute for Nuclear Physics (INFN) – Sezione di Trieste, Via Valerio 2, 34127 Trieste, Italy
³ Institute for Fundamental Physics of the Universe (IFPU), Via Beirut 2, 34151 Grignano (TS), Italy
⁴ Dipartimento di Fisica e Scienze della Terra, Università degli Studi di Ferrara and INFN – Sezione di Ferrara, Via Saragat 1, 44122 Ferrara, Italy
e-mail: luca.pagano@unife.it
⁵ Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405 Orsay, France

Received: 24 January 2023
Accepted: 15 June 2023

Abstract

The optical depth to reionization, τ, is the least constrained parameter of the cosmological Λ cold dark matter (ΛCDM) model. To date, its most precise value is inferred from large-scale polarized cosmic microwave background (CMB) power spectra from the High Frequency Instrument (HFI) aboard the Planck satellite. These maps are known to contain significant contamination by residual non-Gaussian systematic effects, which are hard to model analytically. Therefore, robust constraints on τ are currently obtained through an empirical cross-spectrum likelihood built from simulations. In this paper, we present a likelihood-free inference of τ from polarized Planck HFI maps which, for the first time, is fully based on neural networks (NNs). NNs have the advantage of not requiring an analytical description of the data and can be trained on state-of-the-art simulations, combining the information from multiple channels. By using Gaussian sky simulations and Planck SRoll2 simulations, including CMB, noise, and residual instrumental systematic effects, we trained, tested, and validated NN models considering different setups. We inferred the value of τ directly from Stokes Q and U maps at ∼4° pixel resolution, without computing angular power spectra. On Planck data, we obtained τ_NN = 0.0579 ± 0.0082, which is compatible with current EE cross-spectrum results but with a ∼30% larger uncertainty, which can be assigned to the inherent nonoptimality of our estimator and to the retraining procedure applied to avoid biases. While this paper does not improve on current cosmological constraints on τ, our analysis represents a first robust application of NN-based inference on real data, and highlights its potential as a promising tool for complementary analysis of near-future CMB experiments, also in view of the ongoing challenge to achieve the first detection of primordial gravitational waves.